Built-in selection or confounder bias? Dynamic Landmarking in matched propensity score analyses.

ABSTRACT:

Background

Propensity score matching has become a popular method for estimating causal treatment effects in non-randomized studies. However, for time-to-event outcomes, the estimation of hazard ratios based on propensity scores can be challenging if omitted or unobserved covariates are present. Not accounting for such covariates could lead to treatment estimates, differing from the estimate of interest. However, researchers often do not know whether (and, if so, which) covariates will cause this divergence.

Methods

To address this issue, we extended a previously described method, Dynamic Landmarking, which was originally developed for randomized trials. The method is based on successively deletion of sorted observations and gradually fitting univariable Cox models. In additi

SUBMITTER: Strobel A 

PROVIDER: S-EPMC11662801 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature